Pub Date : 2023-01-01DOI: 10.5220/0011927700003414
Hui Liu, Tingting Xue, Tanja Schultz
{"title":"On a Real Real-Time Wearable Human Activity Recognition System","authors":"Hui Liu, Tingting Xue, Tanja Schultz","doi":"10.5220/0011927700003414","DOIUrl":"https://doi.org/10.5220/0011927700003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"44 1","pages":"711-720"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85205311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011675400003414
Rama Krishna Thelagathoti, Hesham H. Ali
{"title":"A Correlation Network Model for Analyzing Mobility Data in Depression Related Studies","authors":"Rama Krishna Thelagathoti, Hesham H. Ali","doi":"10.5220/0011675400003414","DOIUrl":"https://doi.org/10.5220/0011675400003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"143 1","pages":"416-423"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85345921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011671400003414
B. Consoli, Renata Vieira, Rafael Heitor Bordini
{"title":"Benchmarking the BRATECA Clinical Data Collection for Prediction Tasks","authors":"B. Consoli, Renata Vieira, Rafael Heitor Bordini","doi":"10.5220/0011671400003414","DOIUrl":"https://doi.org/10.5220/0011671400003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"18 1","pages":"338-345"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82830511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011696000003414
Gerda Graciela Rodrigues de Oliveira, Cristiane Nobre
{"title":"The Use of Machine Learning to Predict Hospitalization of Covid-19: A Case Study in the State of Minas Gerais - Brazil","authors":"Gerda Graciela Rodrigues de Oliveira, Cristiane Nobre","doi":"10.5220/0011696000003414","DOIUrl":"https://doi.org/10.5220/0011696000003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"38 1","pages":"392-399"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81667660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011794100003414
Emma Kwint, Anna Zoet, Katsiaryna Labunets, S. Brinkkemper
{"title":"How Different Elements of Audio Affect the Word Error Rate of Transcripts in Automated Medical Reporting","authors":"Emma Kwint, Anna Zoet, Katsiaryna Labunets, S. Brinkkemper","doi":"10.5220/0011794100003414","DOIUrl":"https://doi.org/10.5220/0011794100003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"24 1","pages":"179-187"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82814935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011677600003414
Roland Stenger, Sebastian Löns, Feline Hamami, Nele Sophie Brügge, T. Bäumer, Sebastian J. F. Fudickar
: We present an extended head pose estimation algorithm, which is trained exclusively on synthesized human avatars. Having five degrees of freedom to describe such head poses, this task can be regarded as being more complex than predicting the absolute rotation only with three degrees of freedom, which is commonly known as head pose estimation. Due to the lack of labeled data sets containing such complex head poses, we created a data set, consisting of renderings of avatars. With this extension, we take a step towards an algorithm that can make a qualitative assessment of cervical dystonia. Its symptomatic consists of an involuntary twisted head posture, which can be described by those five degrees of freedom. We trained an EfficientNetB2 and evaluated the results with the mean absolute error (MAE). Such estimation is possible, but the performance works differently well for the five degrees of freedom, with an MAE between 1.71° and 6.55°. By visually randomizing the domain of the avatars, the gap between real subject photos and the simulated ones might tend to be smaller and enables our algorithm being used on real photos in the future, while being trained on renderings only.
{"title":"Extended Head Pose Estimation on Synthesized Avatars for Determining the Severity of Cervical Dystonia","authors":"Roland Stenger, Sebastian Löns, Feline Hamami, Nele Sophie Brügge, T. Bäumer, Sebastian J. F. Fudickar","doi":"10.5220/0011677600003414","DOIUrl":"https://doi.org/10.5220/0011677600003414","url":null,"abstract":": We present an extended head pose estimation algorithm, which is trained exclusively on synthesized human avatars. Having five degrees of freedom to describe such head poses, this task can be regarded as being more complex than predicting the absolute rotation only with three degrees of freedom, which is commonly known as head pose estimation. Due to the lack of labeled data sets containing such complex head poses, we created a data set, consisting of renderings of avatars. With this extension, we take a step towards an algorithm that can make a qualitative assessment of cervical dystonia. Its symptomatic consists of an involuntary twisted head posture, which can be described by those five degrees of freedom. We trained an EfficientNetB2 and evaluated the results with the mean absolute error (MAE). Such estimation is possible, but the performance works differently well for the five degrees of freedom, with an MAE between 1.71° and 6.55°. By visually randomizing the domain of the avatars, the gap between real subject photos and the simulated ones might tend to be smaller and enables our algorithm being used on real photos in the future, while being trained on renderings only.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"25 1","pages":"354-359"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88263003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011921900003414
Camilla Scapicchio, E. Ballante, F. Brero, R. F. Cabini, A. Chincarini, M. Fantacci, Silvia Figini, A. Lascialfari, Francesca Lizzi, I. Postuma, A. Retico
,
，
{"title":"Integration of a Deep Learning-Based Module for the Quantification of Imaging Features into the Filling-in Process of the Radiological Structured Report","authors":"Camilla Scapicchio, E. Ballante, F. Brero, R. F. Cabini, A. Chincarini, M. Fantacci, Silvia Figini, A. Lascialfari, Francesca Lizzi, I. Postuma, A. Retico","doi":"10.5220/0011921900003414","DOIUrl":"https://doi.org/10.5220/0011921900003414","url":null,"abstract":",","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"1 1","pages":"663-670"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75392635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011665600003414
Akito Yamamoto, E. Kimura, T. Shibuya
: As the amount of biomedical and healthcare data increases, data mining for medicine becomes more and more important for health improvement. At the same time, privacy concerns in data utilization have also been growing. The key concepts for privacy protection are k -anonymity and differential privacy, but k -anonymity alone cannot protect personal presence information, and differential privacy alone would leak the identity. To promote data sharing throughout the world, universal methods to release the entire data while satisfying both concepts are required, but such a method does not yet exist. Therefore, we propose a novel privacy-preserving method, ( ε , k ) -Randomized Anonymization. In this paper, we first present two methods that compose the Randomized Anonymization method. They perform k -anonymization and randomized response in sequence and have adequate randomness and high privacy guarantees, respectively. Then, we show the algorithm for ( ε , k ) -Randomized Anonymization, which can provide highly accurate outputs with both k -anonymity and differential privacy. In addition, we describe the analysis procedures for each method using an inverse matrix and expectation-maximization (EM) algorithm. In the experiments, we used real data to evaluate our methods’ anonymity, privacy level, and accuracy. Furthermore, we show several examples of analysis results to demonstrate high utility of the proposed methods.
{"title":"(ε, k)-Randomized Anonymization: ε-Differentially Private Data Sharing with k-Anonymity","authors":"Akito Yamamoto, E. Kimura, T. Shibuya","doi":"10.5220/0011665600003414","DOIUrl":"https://doi.org/10.5220/0011665600003414","url":null,"abstract":": As the amount of biomedical and healthcare data increases, data mining for medicine becomes more and more important for health improvement. At the same time, privacy concerns in data utilization have also been growing. The key concepts for privacy protection are k -anonymity and differential privacy, but k -anonymity alone cannot protect personal presence information, and differential privacy alone would leak the identity. To promote data sharing throughout the world, universal methods to release the entire data while satisfying both concepts are required, but such a method does not yet exist. Therefore, we propose a novel privacy-preserving method, ( ε , k ) -Randomized Anonymization. In this paper, we first present two methods that compose the Randomized Anonymization method. They perform k -anonymization and randomized response in sequence and have adequate randomness and high privacy guarantees, respectively. Then, we show the algorithm for ( ε , k ) -Randomized Anonymization, which can provide highly accurate outputs with both k -anonymity and differential privacy. In addition, we describe the analysis procedures for each method using an inverse matrix and expectation-maximization (EM) algorithm. In the experiments, we used real data to evaluate our methods’ anonymity, privacy level, and accuracy. Furthermore, we show several examples of analysis results to demonstrate high utility of the proposed methods.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"184 6 1","pages":"287-297"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81046703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011593000003414
K. Smarsly, Yousuf Al-Hakim, P. Peralta, S. Beier, C. Klümper
{"title":"A Systematic Review and Recommendation of Software Architectures for SARS-CoV-2 Monitoring","authors":"K. Smarsly, Yousuf Al-Hakim, P. Peralta, S. Beier, C. Klümper","doi":"10.5220/0011593000003414","DOIUrl":"https://doi.org/10.5220/0011593000003414","url":null,"abstract":"","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"59 1","pages":"211-217"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77343944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5220/0011797100003414
Diogo Machado, Vítor Costa, Pedro Brandão
: Imbalanced data sets pose a complex problem in data mining. Health related data sets, where the positive class is connected to the existence of an anomaly, are prone to be imbalanced. Data related to diabetes management follows this trend. In the case of diabetes, patients avoid situations of hypo/hyperglycaemia, which is the anomaly we want to detect. The use of balancing methods can provide more examples of the minority class, and assist the classifier by clearing the decision boundary. Nevertheless, each over-sampling and under-sampling method can affect the data set uniquely, which will influence the classifier’s performance. In this work, the authors studied the impact of the most known data-balancing methods applied to the Ohio and St. Louis diabetes related data sets. The best and most robust approach was the use of ENN with SMOTE. This hybrid method produced significant performance gains on all the performed tests. ENN in particular had a meaningful impact on all the tests. Given the limited volume of glycaemia-based data available for diabetes management, over-sampling methods would be expected to have a greater role in improving the classifier’s performance. In our experiments, the clearing of noise values by the under-sampling methods, produced better results.
{"title":"Using Balancing Methods to Improve Glycaemia-Based Data Mining","authors":"Diogo Machado, Vítor Costa, Pedro Brandão","doi":"10.5220/0011797100003414","DOIUrl":"https://doi.org/10.5220/0011797100003414","url":null,"abstract":": Imbalanced data sets pose a complex problem in data mining. Health related data sets, where the positive class is connected to the existence of an anomaly, are prone to be imbalanced. Data related to diabetes management follows this trend. In the case of diabetes, patients avoid situations of hypo/hyperglycaemia, which is the anomaly we want to detect. The use of balancing methods can provide more examples of the minority class, and assist the classifier by clearing the decision boundary. Nevertheless, each over-sampling and under-sampling method can affect the data set uniquely, which will influence the classifier’s performance. In this work, the authors studied the impact of the most known data-balancing methods applied to the Ohio and St. Louis diabetes related data sets. The best and most robust approach was the use of ENN with SMOTE. This hybrid method produced significant performance gains on all the performed tests. ENN in particular had a meaningful impact on all the tests. Given the limited volume of glycaemia-based data available for diabetes management, over-sampling methods would be expected to have a greater role in improving the classifier’s performance. In our experiments, the clearing of noise values by the under-sampling methods, produced better results.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"92 1","pages":"188-198"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83745569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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